SoroushVahidi/scidocs huggingface dataset:
Explaining the “ Identifiable Victim Effect ”
(1997)

It is widely believed that people are willing to expend greater resources to save the lives of identified victims than to save equal numbers of unidentified or statistical victims. There are many possible causes of this disparity which have not been enumerated previously or tested empirically. We discuss four possible causes of the “identifiable victim effect” and present the results of two studies which indicate that the most important cause of the disparity in treatment of identifiable and statistical lives is that, for identifiable victims, a high proportion of those at risk can be saved.
Efficiency optimization of half-bridge series resonant inverter with asymmetrical duty cycle control for domestic induction heating
(2009)

In this paper, a method to improve efficiency in half-bridge series resonant inverter applied to domestic induction heating is presented. Low and medium output powers required for this application implies the use of higher switching frequencies, which leads to an efficiency decrease. Asymmetrical duty cycle (ADC) modulation scheme is proposed to improve efficiency due to its switching frequency reduction and absence of additional hardware requirements. Study methodology comprises, in a first step, a theoretical analysis of power balance as a function of control parameters: duty cycle and switching frequency. In addition, restrictions due to snubber and dead time, and variability of the induction loads have been considered. Afterwards, an efficiency analysis has been carried out to determine the optimum operation point. Switching and conduction losses have been calculated to examine global importance of each one for different switching devices. ADC modulation efficiency improvement is achieved by means of a switching frequency reduction, mainly at low-medium power range and low quality factor (Q) loads. The analytical results obtained with this study have been validated through an induction heating test-bench. A discrete 3-kW RL load has been designed to emulate a typical induction heating load. Then, a commercial induction heating inverter is used to evaluate ADC modulation scheme.
Vehicle Detection and Compass Applications using AMR Magnetic Sensors
(1999)

The earliest magnetic field detectors allowed navigation over trackless oceans by sensing the earth’s magnetic poles. Magnetic field sensing has vastly expanded as industry has adapted a variety of magnetic sensors to detect the presence, strength, or direction of magnetic fields not only from the earth, but also from permanent magnets, magnetized soft magnets, vehicle disturbances, brain wave activity, and fields generated from electric currents. Magnetic sensors can measure these properties without physical contact and have become the eyes of many industrial and navigation control systems. This paper will describe the current state of magnetic sensing within the earth’s field range and how these sensors are applied. Several applications will be presented for magnetic sensing in systems with emphasis on vehicle detection and navigation based on magnetic fields.
Flying Eyes and Hidden Controllers: A Qualitative Study of People's Privacy Perceptions of Civilian Drones in The US
(2016)

Drones are unmanned aircraft controlled remotely or operated autonomously. While the extant literature suggests that drones can in principle invade people’s privacy, little is known about how people actually think about drones. Drawing from a series of in-depth interviews conducted in the United States, we provide a novel and rich account of people’s pri­ vacy perceptions of drones for civilian uses both in general and under specific usage scenarios. Our informants raised both physical and information privacy issues against government, organization and individual use of drones. Informants’ reason­ ing about the acceptance of drone use was in part based on whether the drone is operating in a public or private space. However, our informants differed significantly in their defini­ tions of public and private spaces. While our informants’ pri­ vacy concerns such as surveillance, data collection and shar­ ing have been raised for other tracking technologies such as camera phones and closed-circuit television (CCTV), our in­ terviews highlight two heightened issues of drones: (1) pow­ erful yet inconspicuous data collection, (2) hidden and inac­ cessible drone controllers. These two aspects of drones render some of people’s existing privacy practices futile (e.g., notice recording and ask controllers to stop or delete the recording). Some informants demanded notifications of drones near them and expected drone controllers asking for their explicit per­ missions before recording. We discuss implications for future privacy-enhancing drone designs.
Reduced graphene oxide–silver nanoparticle nanocomposite: a potential anticancer nanotherapy
(2015)

BACKGROUND
Graphene and graphene-based nanocomposites are used in various research areas including sensing, energy storage, and catalysis. The mechanical, thermal, electrical, and biological properties render graphene-based nanocomposites of metallic nanoparticles useful for several biomedical applications. Epithelial ovarian carcinoma is the fifth most deadly cancer in women; most tumors initially respond to chemotherapy, but eventually acquire chemoresistance. Consequently, the development of novel molecules for cancer therapy is essential. This study was designed to develop a simple, non-toxic, environmentally friendly method for the synthesis of reduced graphene oxide-silver (rGO-Ag) nanoparticle nanocomposites using Tilia amurensis plant extracts as reducing and stabilizing agents. The anticancer properties of rGO-Ag were evaluated in ovarian cancer cells.


METHODS
The synthesized rGO-Ag nanocomposite was characterized using various analytical techniques. The anticancer properties of the rGO-Ag nanocomposite were evaluated using a series of assays such as cell viability, lactate dehydrogenase leakage, reactive oxygen species generation, cellular levels of malonaldehyde and glutathione, caspase-3 activity, and DNA fragmentation in ovarian cancer cells (A2780).


RESULTS
AgNPs with an average size of 20 nm were uniformly dispersed on graphene sheets. The data obtained from the biochemical assays indicate that the rGO-Ag nanocomposite significantly inhibited cell viability in A2780 ovarian cancer cells and increased lactate dehydrogenase leakage, reactive oxygen species generation, caspase-3 activity, and DNA fragmentation compared with other tested nanomaterials such as graphene oxide, rGO, and AgNPs.


CONCLUSION
T. amurensis plant extract-mediated rGO-Ag nanocomposites could facilitate the large-scale production of graphene-based nanocomposites; rGO-Ag showed a significant inhibiting effect on cell viability compared to graphene oxide, rGO, and silver nanoparticles. The nanocomposites could be effective non-toxic therapeutic agents for the treatment of both cancer and cancer stem cells.
Transfer Learning for Music Classification and Regression Tasks
(2017)

In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as lowand high-level music features.
Detecting Deceptive Behavior via Integration of Discriminative Features From Multiple Modalities
(2017)

Deception detection has received an increasing amount of attention in recent years, due to the significant growth of digital media, as well as increased ethical and security concerns. Earlier approaches to deception detection were mainly focused on law enforcement applications and relied on polygraph tests, which had proved to falsely accuse the innocent and free the guilty in multiple cases. In this paper, we explore a multimodal deception detection approach that relies on a novel data set of 149 multimodal recordings, and integrates multiple physiological, linguistic, and thermal features. We test the system on different domains, to measure its effectiveness and determine its limitations. We also perform feature analysis using a decision tree model, to gain insights into the features that are most effective in detecting deceit. Our experimental results indicate that our multimodal approach is a promising step toward creating a feasible, non-invasive, and fully automated deception detection system.
Inferring Undiscovered Public Knowledge by Using Text Mining-driven Graph Model
(2014)

Due to the recent development of Information Technology, the number of publications is increasing exponentially. In response to the increasing number of publications, there has been a sharp surge in the demand for replacing the existing manual text data processing by an automatic text data processing. Swanson proposed ABC model [1] on the top of text mining as a part of literature-based knowledge discovery for finding new possible biomedical hypotheses about three decades ago. The following clinical scholars proved the effectiveness of the possible hypotheses found by ABC model [2]. Such effectiveness let scholars try various literature-based knowledge discovery approaches [3, 4, 5]. However, their trials are not fully automated but hybrids of automatic and manual processes. The manual process requires the intervention of experts. In addition, their trials consider a single perspective. Even trials involving network theory have difficulties in mal-understanding the entire network structure of the relationships among concepts and the systematic interpretation on the structure [6, 7]. Thus, this study proposes a novel approach to discover various relationships by extending the intermediate concept B to a multi-leveled concept. By applying a graph-based path finding method based on co-occurrence and the relational entities among concepts, we attempt to systematically analyze and investigate the relationships between two concepts of a source node and a target node in the total paths. For the analysis of our study, we set our baseline as the result of Swanson [8]'s work. This work suggested the intermediate concept or terms between Raynaud's disease and fish oils as blood viscosity, platelet aggregability, and vasconstriction. We compared our results of intermediate concepts with these intermediate concepts of Swanson's. This study provides distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.
Failure detection and consensus in the crash-recovery model
(2000)

Summary. We study the problems of failure detection and consensus in asynchronous systems in which processes may crash and recover, and links may lose messages. We first propose new failure detectors that are particularly suitable to the crash-recovery model. We next determine under what conditions stable storage is necessary to solve consensus in this model. Using the new failure detectors, we give two consensus algorithms that match these conditions: one requires stable storage and the other does not. Both algorithms tolerate link failures and are particularly efficient in the runs that are most likely in practice – those with no failures or failure detector mistakes. In such runs, consensus is achieved within $3 \delta$ time and with 4 n messages, where $\delta$ is the maximum message delay and n is the number of processes in the system.
Preimages for Step-Reduced SHA-2
(2009)

In this paper, we present preimage attacks on up to 43step SHA-256 (around 67% of the total 64 steps) and 46-step SHA-512 (around 57.5% of the total 80 steps), which significantly increases the number of attacked steps compared to the best previously published preimage attack working for 24 steps. The time complexities are 2, 2 for finding pseudo-preimages and 2, 2 compression function operations for full preimages. The memory requirements are modest, around 2 words for 43-step SHA-256 and 46-step SHA-512. The pseudo-preimage attack also applies to 43-step SHA-224 and SHA-384. Our attack is a meet-in-the-middle attack that uses a range of novel techniques to split the function into two independent parts that can be computed separately and then matched in a birthday-style phase.
Through-Wall Opportunistic Sensing System Utilizing a Low-Cost Flat-Panel Array
(2011)

A UWB through-wall imaging system is proposed based on a planar low profile aperture array operating from 0.9 GHz to 2.3 GHz. The goal is to provide a lightweight, fixed array to serve as an alternative to synthetic aperture radars (SAR) that require continuous array movement while collecting data. The proposed system consists of 12 dual-linear printed elements arranged within a triangular lattice, each forming a “flower” shape and backed by a ground plane. The array delivers half-space radiation with wideband performance, necessary for imaging applications. UWB capability is realized by suppressing grating lobes via the introduction of virtual phase centers interwoven within the actual array feeds. The proposed system is demonstrated for through-wall imaging via a non-coherent process. Distinctively, several coherent images are forged from various fixed aperture locations (referred to as “snapshot” locations) and appropriately combined to create a composite scene image. In addition to providing a unique wideband imaging capability (as an alternative to SAR), the system is portable and inexpensive for collecting/storing scattering data. The array design and data collection system is described, and several through-wall images are presented to demonstrate functionality.
Maximum likelihood linear transformations for HMM-based speech recognition
(1998)

This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple bias, strict linear feature-space transformations are inappropriate in this case. Hence, only model-based linear transforms are considered. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii) constrained, which requires the variance transform to have the same form as the mean transform (sometimes referred to as feature-space transforms). Re-estimation formulae for all appropriate cases of transform are given. This includes a new and e cient \full" variance transform and the extension of the constrained model-space transform from the simple diagonal case to the full or block-diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time e ciency, and use for speaker adaptive training. The recognition performance of the two model-space transforms on a large vocabulary speech recognition task using incremental adaptation is investigated. In addition, initial experiments using the constrained model-space transform for speaker adaptive training are detailed.
Semantic-Based Location Recommendation With Multimodal Venue Semantics
(2015)

In recent years, we have witnessed a flourishing of location -based social networks. A well-formed representation of location knowledge is desired to cater to the need of location sensing, browsing, navigation and querying. In this paper, we aim to study the semantics of point-of-interest (POI) by exploiting the abundant heterogeneous user generated content (UGC) from different social networks. Our idea is to explore the text descriptions, photos, user check-in patterns, and venue context for location semantic similarity measurement. We argue that the venue semantics play an important role in user check-in behavior. Based on this argument, a unified POI recommendation algorithm is proposed by incorporating venue semantics as a regularizer. In addition to deriving user preference based on user-venue check-in information, we place special emphasis on location semantic similarity. Finally, we conduct a comprehensive performance evaluation of location semantic similarity and location recommendation over a real world dataset collected from Foursquare and Instagram. Experimental results show that the UGC information can well characterize the venue semantics, which help to improve the recommendation performance.
SyncGC: A Synchronized Garbage Collection Technique for Reducing Tail Latency in Cassandra
(2018)

Data-center applications running on distributed databases often suffer from unexpectedly high response time fluctuation which is caused by long tail latency. In this paper, we find that long tail latency of user writes is mainly created by the interference with garbage collection (GC) tasks running in various system layers. In order to address the tail latency problem, we propose a synchronized garbage collection technique, called SyncGC. By scheduling multiple GC instances to execute in sync with each other in an overlapped manner, SyncGC prevents user requests from being interfered with GC instances, thereby minimizing their negative impacts on tail latency. Our experimental results with Cassandra show that SyncGC reduces the 99.99th-percentile tail latency and the maximum latency by 35% and 37%, on average, respectively.
Experimental validation of dual PPG local pulse wave velocity probe
(2017)

A novel dual photoplethysmograph (PPG) probe and measurement system for local pulse wave velocity (PWV) is proposed and demonstrated. The developed probe design employs reflectance PPG transducers for non-invasive detection of blood pulse propagation waveforms from two adjacent measurement points (28 mm apart). Transit time delay between the continuously acquired dual pulse waveform was used for beat-to-beat local PWV measurement. An in-vivo experimental validation study was conducted on 10 healthy volunteers (8 male and 2 female, 21 to 33 years of age) to validate the PPG probe design and developed local PWV measurement system. The proposed system was able to measure carotid local PWV from multiple subjects. Beat-to-beat variation of baseline carotid PWV was less than 7.5% for 7 out of 10 subjects, a maximum beat-to-beat variation of 16% was observed during the study. Variation in beat-to-beat carotid local PWV and brachial blood pressure (BP) values during post-exercise recovery period was also examined. A statistically significant correlation between intra-subject local PWV variation and brachial BP parameters was observed (r > 0.85, p < 0.001). The results demonstrated the feasibility of proposed PPG probe for continuous beat-to-beat local PWV measurement from the carotid artery. Such a non-invasive local PWV measurement unit can be potentially used for continuous ambulatory BP measurements.
Millimeter-Wave TE20-Mode SIW Dual-Slot-Fed Patch Antenna Array With a Compact Differential Feeding Network
(2018)

A millimeter-wave series–parallel patch antenna array is presented, in which the dual-slot feeding structure is handily implemented using the intrinsic field distribution of TE<sub>20</sub> mode in substrate-integrated waveguide (SIW). One 28 GHz patch antenna element fed by the TE<sub>20</sub>-mode SIW is first designed, achieving a 10 dB impedance bandwidth of 10.2% and a simulated peak gain of 6.48 dBi. Based on the antenna element, a <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array with a compact series–parallel differential feeding network is developed accordingly. Due to the novel compact SIW-based series–parallel feeding network, the antenna array can achieve superior radiation performances, which is the highlight of this communication. The simulation and measurement results of the proposed antenna array are in good agreement, demonstrating a performance of 8.5% impedance bandwidth, 19.1 dBi peak gain, symmetrical radiation patterns, and low cross-polarization levels (−30 dB in E-plane and −25 dB in H-plane) in the operating frequency band of 26.65–29.14 GHz.
Perceived parental social support and academic achievement: an attachment theory perspective.
(1994)

The study tested the extent to which parental social support predicted college grade point average among undergraduate students. A sample of 418 undergraduates completed the Social Provisions Scale--Parent Form (C.E. Cutrona, 1989) and measures of family conflict and achievement orientation. American College Testing Assessment Program college entrance exam scores (ACT; American College Testing Program, 1986) and grade point average were obtained from the university registrar. Parental social support, especially reassurance of worth, predicted college grade point average when controlling for academic aptitude (ACT scores), family achievement orientation, and family conflict. Support from parents, but not from friends or romantic partners, significantly predicted grade point average. Results are interpreted in the context of adult attachment theory.
Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony
(2001)

The quantification of phase synchrony between neuronal signals is of crucial importance for the study of large-scale interactions in the brain. Two methods have been used to date in neuroscience, based on two distinct approaches which permit a direct estimation of the instantaneous phase of a signal [Phys. Rev. Lett. 81 (1998) 3291; Human Brain Mapping 8 (1999) 194]. The phase is either estimated by using the analytic concept of Hilbert transform or, alternatively, by convolution with a complex wavelet. In both methods the stability of the instantaneous phase over a window of time requires quantification by means of various statistical dependence parameters (standard deviation, Shannon entropy or mutual information). The purpose of this paper is to conduct a direct comparison between these two methods on three signal sets: (1) neural models; (2) intracranial signals from epileptic patients; and (3) scalp EEG recordings. Levels of synchrony that can be considered as reliable are estimated by using the technique of surrogate data. Our results demonstrate that the differences between the methods are minor, and we conclude that they are fundamentally equivalent for the study of neuroelectrical signals. This offers a common language and framework that can be used for future research in the area of synchronization.